Can Magistral Small 2507 run on Quadro RTX 8000 48GB?

YES — Runs Great

S90Excellent

Estimated from fit model

Magistral Small 2507 needs ~23.1 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~34 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 23.1 GB, 34.0 tok/s, Runs well

23.1 GB required48.0 GB available

48% VRAM used

Fit status

Runs well

Decode

34.0 tok/s

TTFT

5686 ms

Safe context

131K

Memory

23.1 GB / 48.0 GB

Memory breakdown

Weights14.6 GB

KV Cache2.4 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsMagistral Small 2507 on Quadro RTX 8000 48GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 34.0 tok/s decode · 5.7s TTFT (warm) · 85 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	34.0 tok/s	3102 ms	131K
Coding	S	Runs well	34.0 tok/s	5686 ms	131K
Agentic Coding	S	Runs well	34.0 tok/s	8271 ms	131K
Reasoning	S	Runs well	34.0 tok/s	6720 ms	131K
RAG	S	Runs well	34.0 tok/s	10338 ms	131K

Inference speed

Magistral Small 2507 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Magistral Small 2507 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~88 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	88.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	56.3	Tight
RX 7900 XTX 24GB	24 GB	Q4_K_M	50.8	Tight
RTX 3090 24GB	24 GB	Q4_K_M	48.1	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	40.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	36.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	36.8	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	34.1	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	32.3	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.2	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	21.3	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	17.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	16.2	Fits
RTX 4070 12GB	12 GB	Q4_K_M	7.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	4.7	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.2	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Magistral Small 2507 (24B params) fits at each quantization level on Quadro RTX 8000 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	9.4 GB	Low	A84
Q3_K_S	3	11.8 GB	Low	A85
NVFP4	4	13.4 GB	Medium	S85
Q4_K_M	4	14.6 GB	Medium	S86
Q5_K_M	5	17.3 GB	High	S87
Q6_K	6	19.7 GB	High	S87
Q8_0Best for your GPU	8	25.7 GB	Very High	S90
F16	16	49.2 GB	Maximum	F0

Get started

Copy-paste commands to run Magistral Small 2507 on your machine.

Run

ollama run magistral

Your hardware

More models your Quadro RTX 8000 48GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	70.1 tok/s
Qwen 3.5 27B	27B	S	30.4 tok/s
Qwen 3.6 27B	27B	S	30.5 tok/s
Qwen 3.6 35B A3B	35B	S	58.9 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	72.5 tok/s

Frequently asked questions

Can Quadro RTX 8000 48GB run Magistral Small 2507?

Yes, Quadro RTX 8000 48GB can run Magistral Small 2507 with a S grade (Runs well). Expected decode speed: 34.0 tok/s.

How much VRAM does Magistral Small 2507 need?

Magistral Small 2507 (24B parameters) requires approximately 23.1 GB of memory with Q4_K_M quantization.

What is the best quantization for Magistral Small 2507?

The recommended quantization for Magistral Small 2507 is Q4_K_M, which balances quality and memory efficiency.

What speed will Magistral Small 2507 run at on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Magistral Small 2507 achieves approximately 34.0 tokens per second decode speed with a time-to-first-token of 5686ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run Magistral Small 2507 for coding?

For coding workloads, Magistral Small 2507 on Quadro RTX 8000 48GB receives a S grade with 34.0 tok/s and 131K context.

What context window can Magistral Small 2507 use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Magistral Small 2507 can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for Quadro RTX 8000 48GB See all hardware for Magistral Small 2507

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